Conclusion

Congratulations on completing this guided project!
You learned how to implement “Retrieval Augmented Search”, in Generative AI. You also learned how to work with LLMs, and vector store, how to create Embeddings, and how to integrate everything using Langchain.You created a real application, a chatbot, using Python, Flask, and JavaScript and you packaged and deployed it using containers and Kubernetes. You can share your achievements on LinkedIn, Twitter, and other social media. The guided project detail page has buttons to help you do this.

Next steps

If generative AI and large language models (LLMs) interest you, we encourage you to apply for a free trial of the IBM watsonx.ai. WatsonX is IBM enterprise-ready AI and data platform designed to multiply the impact of AI across your business. The platform comprises three powerful products: the watsonx.ai studio for new foundation models, generative AI and machine learning; and the watsonx.data fit-for-purpose data store, built on an open lakehouse architecture; and the watsonx.governance toolkit, to accelerate AI workflows that are built with responsibility, transparency, and explainability.

Moving forward, dive deeper into chatbot creation. The following guided project can assist you in acquiring the necessary skills for that endeavor.

Create a voice assistant with IBM Watson

Furthermore, you can delve into learning about Langchain and its functionalities. This allows you to add more capabilities to the chatbot, such as analyzing various types of files and generating output plots. Following guided project can be helpful.

Create AI-powered apps with open source LangChain

Author(s)

Sina Nazeri

Talha Siddiqui

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